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TCA
View on CRAN: Click
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Download and install TCA package within the R console
Install from CRAN:
install.packages("TCA")
Install from Github:
library("remotes")
install_github("cran/TCA")
Install by package version:
library("remotes")
install_version("TCA", "1.2.1")
Attach the package and use:
library("TCA")
Maintained by
Elior Rahmani
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-22
Latest Update: 2021-02-14
Description:
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <doi:10.1038/s41467-019-11052-9>.
How to cite:
Elior Rahmani (2019). TCA: Tensor Composition Analysis. R package version 1.2.1, https://cran.r-project.org/web/packages/TCA
Other packages that cited TCA R package
View TCA citation profile
Other R packages that TCA depends,
imports, suggests or enhances
Functions, R codes and Examples using
the TCA R package
Some associated functions: refactor . tca . tcareg . tcasub . tensor . test_data .
Some associated R codes: TCA.R . model_fit.R . refactor.R . utils.R . Full TCA package functions and examples
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